Abstract
In this paper a hierarchical, neural network control architecture of a walking machine is proposed. The neural network is based on the theory of the Cerebellum Model Articulation Controller (CMAC) which is a neuromuscular control system. Some preliminary studies of kinematic control and gait synthesis are presented to demonstrate the effectiveness of the CMAC neural network. After having been trained to learn the multivariable, nonlinear relationships of the leg kinematics and gaits, CMAC is utilized to perform feedforward kinematic control of a quadruped in straight-line walking and step climbing. Simulation examples are provided and discussed. This algorithm can be extended to control other highly nonlinear processes which are hierarchical in nature and cannot be modeled by mathematical equations.
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